The present invention is related to a defect reviewing method and a defect reviewing apparatus, which are capable of reviewing various types of defects occurred in manufacturing processes for manufacturing semiconductor devices, liquid crystal panels, and the like, and are also capable of classifying the reviewed defects.
Since circuit patterns to be formed on semiconductor wafers in manufacturing steps of semiconductor devices are considerably made narrower and narrower, defects which happen to occur in these manufacturing steps may give strong impact on product yields. Thus, it is very important to manage that such defects are not produced in manufacturing stages. Generally speaking, in present manufacturing fields of semiconductor wafers, certain measures capable of improving manufacturing yields have been taken by employing wafer inspection apparatuses and reviewing apparatuses.
A wafer inspection apparatus is employed in order to check that a defect is located at which position on a wafer in a high speed. While a status of a wafer surface is processed as an image by employing either an optical imaging means or a means for irradiating an electron beam so as to image the wafer surface, the acquired image is automatically processed, so that the examining apparatus checks as to whether or not the defect is present on the wafer surface. In the examining apparatus with employment of the optical means, even though presence of a very small defect can be recognized from the detected image, due to a limitation in resolution restricted by a wavelength, a type of this defect can be hardly discriminated in detail. On the other hand, in the examining apparatus with employment of the electron beam, since the highspeed characteristic thereof constitutes the important factor, pixel sizes of an image to be acquired are made as large as possible (namely, resolution of image to be acquired is lowered) so as to reduce amounts of image data. In most cases, even though presence of a defect can be recognized from the detected image having such a low resolution, a type of this defect cannot be discriminated in detail.
On the other hand, a reviewing apparatus is such an apparatus used in order that as to each of defects detected by the inspection apparatus, an image thereof is imaged under such a condition that a pixel size is reduced (namely, under high resolution), and then, this imaged defect is classified. Presently, various sorts of reviewing apparatuses for manually performing, or automatically performing image acquiring process operations and image classifying process operations by way of computers have been commercially available. In these reviewing apparatuses, resolution of images, which is required for executing the classifying operations in sufficiently high precision, is determined based upon defects to be reviewed. In semiconductor manufacturing processes where very fine circuit patterns are made, while there are some possibilities that sizes of defects are reached to the order of several tens of nanometers, such reviewing apparatuses using scanning electron microscopes capable of reducing pixel sizes to several nanometers (will be referred as “review SEMs” hereinafter) have been utilized in an actual field.
As to technical ideas of defect classifications executed in the above-described review SEMs, conventionally, classifiers operable based upon rule bases, and example-based classifiers using statistical pattern recognizing manners are known. A rule-based classifier corresponds to such a system that while a classification rule has been previously formed by utilizing various sorts of natures owned by defects, a classification is carried out based upon this formed classification rule. The classification rule is determined by utilizing natures owned by defects (for instance, surface topograph of defect, shape (either round or rectangular shape) of defect, or dimension (either large or small) of defect). In other words, while the classification rule employs numeral data (will be referred to as “feature value” hereinafter) acquired by calculating the topography or the dimension in the quantitative manner, this classification rule performs the classification by judging as to whether or not a feature value calculated from a defect which should be classified can satisfy a basis defined by this rule.
Also, an example-based classifier operable based upon a statistical pattern recognition corresponds to a statistical classifier with employment of an exemplification sample. This statistical classifier implies the following classifier: That is, while a user has previously defined classes and has previously applied a plurality of sample defect data for each of these defined classes, and also the user has previously calculated various sorts of defect feature values (for example, brightness of defect image, image texture information of defect portion etc.) from these sample defect data, which are conceivable as effective feature values for classifying these defects, the user determines a classification basis (for example, distance in feature value space) based upon a statistical nature of this feature value data. Then, when a classification is carried out, the relevant defect is classified by comparing a feature value calculated from defect data which should be classified with the previously-formed classification basis.
A large number of classifying systems with employment of these classifiers have been developed to be marketed in various types thereof. For example, JP-A-2001-135692 describes such a classification technical idea constructed by combining rule-based classifiers with example-based classifiers. In accordance with this conventional technical idea, in the case that a defect to be classified is classified, first of all, this defect is classified by employing the rule-based classifiers, and thereafter, the defect is classified by using such an example-based classifier which corresponds to the relevant rule class. In general, in the case that a defect is automatically classified into a large number of classes (for example, 10, or more classes) by employing an example-based classifier, the following fact is known: That is, it is practically difficult to achieve sufficiently high performance. However, in the above-explained method, the example-based classifier employs only such classes adapted to the respective rule classification classes, which should be classified. As a result, the conventional classifying system has such a merit that a total number of classes which should be classified by the example-based classifier may be reduced to, for example, 2, or 3 classes.